The barrage of electronic content available over a wide variety of information paths such as electronic retail sites and portals, search engines, electronic news and media services, corporate and private blog and news group postings, social networks etc. has lead to a growing need to filter, organize, categorize, and rank content in more intelligent ways. A typical individual connected online is confronted with more than what s/he can read or digest in a day, let alone an hour. In addition, finding particular types of content, products, and other information is difficult especially when one is not sure of what to search for, because the search results are over-extensive—often creating a problem of finding a needle in a haystack when trying to discover information.
Some social network sites, news, and other online environments support an ability for a user to tag content, typically with words that are nouns, to allow a user to categorize the content perhaps for finding it again in the future, sending to another colleague or friend, or for example, to create a shorthand label for further retrieval. These tags can act as placeholders or links for the content. Users can then use tools, for example, to retrieve all content tagged with a certain noun. One difficulty created by such solutions is that tagging something with a noun does not provide an ability for a user to express his or her opinion about that content; it rather simply allows the user to name that content. In order to express an opinion, typically a ranking mechanism needs to be concurrently provided. Such ranking systems, however, commonly lack uniformity and an ability to communicate to a downstream reader the logic used behind the rank.
Similarly, many online retail sites have created an ability for users to rank products and other offerings, so that other future purchasers, reviewers, etc. can hopefully gain useful insights as to whether such products or offerings would be suitable for their needs. Many such systems allow an extensive text field for hand written comments. These may be laborious and time consuming to read, but often do provide insight as to why the reviewer gave the product the rank s/he did. However, even with such comments available, these ranking systems are in the end limited by inconsistency as to the persons assigning ranking values. For example, a sixteen year old's “4 star” movie may not be the same as a sixty year old's. Thus, typically in the online world, there lack rules for indicating when something ought to be ranked one way versus another.
In addition, much content, for example, that present in news articles, personal blog postings, online reviews, corporate or public websites, inherently contains its own opinion about whatever is the subject of the content. For example, a news article may have a positive or negative slant on a topic. Such sentiment information can be helpful to people wishing to filter searches to exclude or include only certain types of information. For example, a person may only wish to look at positive reviews of a new movie that just hit the theatres. There is presently no accurate way to find out this information. Traditionally, information providers such as search engines have attempted to mine the content, for example, for the presence of certain negative or positive words, to help classify the tone of the content and/or such words and phrases might be included in a text search. The search results are accordingly limited by the lack of consistency of sentiment expressed in the content and that it is difficult to separate negative ratings of content from things like content about negative topics.